Clinicians and researchers increasingly use image data representing biological tissues, e.g., to identify and diagnose tissue anomalies and pathologies. Imaging using optical coherence tomography (OCT) is an imaging method that produces cross-sectional images of tissue morphology (see, e.g., Pieroni et al. (2006) “Ultrahigh resolution optical coherence tomography in non-exclusive age related macular degeneration” Br J Ophthalmol 90(2): 191-7).
In particular, OCT provides excellent visualization of retinal tissue abnormalities using cross-sectional pseudo-color or grayscale images of the tissue reflectivity. However, quantifying the size (e.g., determining the lateral extent, area, and/or volume) of localized retinal tissue abnormalities is not trivial. For example, determining the en face area of a retinal abnormality in OCT data alone (e.g., without reference to a fundus image) involves a user identifying the border of the retinal abnormality in each of many OCT cross-sectional images. This is a very laborious process and is not accurate when the abnormality has multiple loci, a complex or irregular shape, or when the extent of the abnormalities increases laterally. Further, OCT segmentation is typically based on known anatomical tissue layers (e.g., in a normal subject) and thus is not necessarily based on the border of the abnormality.
For example, extant technologies for evaluating a volumetric size (e.g., the volume of the retinal cystoid space) often include steps wherein a clinician views an OCT image and manually segments various anatomical layers (e.g., the inner limiting membrane (ILM) and the retinal pigment epithelium (RPE)) shown in the image. Then, software performs a volumetric calculation, e.g., by a trapezoidal integration of multiple frames of the thickness between the anatomical layers. Using such a technique, while the resultant value includes the volume of the abnormality under examination (e.g., the cystoid space), it also includes the volume of the neighboring (e.g., healthy) tissue that may not be relevant to the calculation related to the abnormality. Further, some extant technologies are associated with measuring the variance of the volume of a tissue from the development of the abnormality by comparison to a normative database. These measurements, however, are confined to the fixed regions and the fixed segmented layers that have to be measured to generate the normative data. In addition, these technologies are limited in that such measurements are not typically made and thus appropriate data are not available.
In addition, quantitative analysis of OCT data has been used in the diagnosis and treatment of macular degeneration (e.g., wet and dry age-related macular degeneration (AMD)). For example, retinal thickness measurements have been used for some patients to monitor the effectiveness of treatment with anti-vascular endothelial growth factor (VEGF) agents (e.g., Ranibizumab marketed as, e.g., “Lucentis”; aflibercept marketed as, e.g., “Eylea”). Measurement of retinal thickness (e.g., measurement of a fixed 1-mm (e.g., 0.1 to 10 mm) diameter region centered on the macula) is used to estimate the subretinal and/or the intraretinal fluid accumulation and is generally accepted by the FDA as an imaging endpoint. In addition, new combination therapies are available that target both VEGF and platelet-derived growth factor (PDGF). While the anti-PDGF treatment is administered to reduce the size of the central neovascularization (CNV) complex associated with macular degeneration, quantitative analysis of this treatment in human clinical trials is presently limited. Several preliminary studies have attempted to quantify the lateral extent (e.g., the area) of the CNV lesion complex using fluorescein angiography images but have not quantified the area or volume of a CNV lesion based on OCT data (see, e.g., Jaffe et al. (2015) “A phase 1 study of intravitreous E10030 in combination with Ranibizumab in neovascular AMD” Ophthalmology (Manuscript no. 2015-470, in press); Boyer (2009) “Combination inhibition of PDGF and VEGF for treatment of neovsacular AMD”, ARVO abstract).
Area measurements of retina lesions also find use as anatomic endpoints, e.g., for monitoring and treating geographic atrophy (GA) of the retina pigment epithelium (RPE) in AMD. In particular, an increase in the area of the GA region over time is considered a measure of disease progression. In addition, a clinically important measure is the proximity of the boundary of the GA (as measured by loss of the RPE and/or disappearance of the external limiting membrane layer of the retina) to the retinal area for the center of vision, the fovea. A treatment that slows the progression of GA (e.g., slows the increase of the area of the GA region as a function of time) and/or delays the invasion of the GA region toward and/or into the fovea may preserve vision and/or minimize the loss of vision. Numerous treatments for GA are under clinical investigation at this time and would benefit from technologies that measure and/or monitor the size and/or change in size of a GA region. Current technologies for tracking GA progression in both clinical care and clinical research are based on measuring the area of a GA region using en face imaging modalities such as, e.g., retina photographs, fundus autofluorescence, fluorescein angiograms, etc. Further, present OCT-based measurements of GA are based on voxel projection images (see, e.g., Hu (2013) “Segmentation of Geographic Atrophy in Spectral-Domain Optical Coherence Tomography and Fundus Autofluorescence Images” Invest Ophth Vis Sci 54: 8375-83; Yehoshua (2014) “Comparison of Geographic Atrophy Measurements from the OCT Fundus Image and the Sub-RPE Slab Image” Ophthalmic Surg Lasers Imaging Retina 44: 127-32).
Thus, although OCT data are valuable to clinicians and researchers, the utility of OCT technologies would benefit from improved image analysis for measuring the sizes of tissue anomalies and pathologies, e.g., by directly correlating eye microstructures using three-dimensional (e.g., volumetric) metrics and two-dimensional (e.g., en face) display of data.